编程作业代写✓原创优质代码✓保分85%+

编程代写价格贵吗？

1. 留学生不同的在校学术阶段会影响价格，不同于高中作业，大学以及硕士的代写要求更需要老师具备专业知识，难度会更高，定价也会更高。
2. 交付时间-相对于一个月交付的任务，12个小时就要求交稿的任务更需要协调时间以及考验老师的能力，费用区间会更高一些。
3. 代写项目的难度-想日常的homework，要求比较少，花两三个小时就能做完，价格会实惠很多。如果是重要的assignment或者是major paper，占整个课程中的绩点比重很高，难度大，代写价格也会高。

1.专业性高

2.收费合理

3.准确率高

4. 准时守时，高效沟通

5. 完善的售后流程

下面是一个大数据编程作业代写的案例，熟悉大数据项目所需要的理论知识和实用的软件及工具。

Data choice. Choose any dataset from the repository that has at least five attributes, and for which the default task is classification. Transform this dataset into an appropriate one to load into your chosen analytics software.

Background information. Write a description of the dataset and project. Provide an overview of what the dataset is about, including from where and how it has been gathered, and for what purpose.

Data description. Describe how many instances does the dataset contain, how many attributes there are in the dataset, their names, and include which is the class attribute.

Include in your description details of any missing values, and any other relevant characteristics. Use appropriate pandas functions to initially analyse the data, for instance descriptive statistics of each attribute, including description of the range of possible values of the attributes, and visualise these in a graphical format.

Initial analysis. You will need to make decisions about which features to include in your dataframe, and how to deal with missing values (if they exist). You might need preprocess the dataset attributes. Useful techniques will include remove certain attributes, exploring different ways of discretizing continuous attributes and replacing missing values. Discretizing is the conversion of numeric attributes into “nominal” ones by binning numeric values into intervals. If you replaced missing values explain what strategy you used to select a replacement of the missing values.

Service Scope

C|C++|Java|Python|Matlab|Android|Jsp|Prolo